The present invention generally relates to wireless communication devices, and particularly relates to suppressing interference from a received signal using channelization code power estimates.
Direct Sequence Code Division Multiple Access (DS-CDMA) systems use two different codes for separating devices and/or channels when transmitting data. Scrambling codes are pseudo-noise sequences used to separate base stations, cells or sectors from each other in the downlink. In the uplink, scrambling codes separate users. Channelization codes are orthogonal sequences used to separate different channels in a particular cell or sector in the downlink or to separate different parallel streams of data intended for one user in either the uplink or downlink. As such, channelization codes channelize user-specific data onto respective channels and scrambling codes scramble the channelized data. Power allocated to each channelization code typically differs and depends on channel conditions. For example, channelization code power may be reduced for those channels having good signal transmission characteristics and increased for those channels having poor signal transmission characteristics. Alternatively, more radio resources may be allocated to high quality channels and less to low quality channels.
Multipath fading causes loss of orthogonality between different channelization codes, thus causing signal interference. This interference can be self-interference (e.g. between codes assigned to the same user) as well as inter-user-interference. Signal interference limits performance in DS-CDMA systems such as Wideband-CDMA (W-CDMA), High Speed Downlink Packet Access (HSDPA), CDMA2000 and 1XEV-DO systems. Particularly, capacity and high data-rate coverage are adversely affected. Signal interference may be self-induced where other desired symbols sent in parallel (multicode) or in series (previous, next symbols) interfere with a symbol of interest. Other-user symbols also cause interference and may be sent from the same base station (own-cell interference) or from a different base station (other-cell interference).
Conventional wireless receivers account for impairment (interference and noise) correlations between interfering signals and a signal of interest when processing a received signal. Accounting for impairment correlations enables a receiver to better suppress interference from a received signal, thus improving performance. For example, in a Generalized RAKE (G-RAKE) receiver, some signal processing “fingers” are placed on signal path delays of a multipath fading channel for optimizing signal energy collection while other fingers are placed off signal path delays to suppress interference. Finger outputs are combined to form symbol estimates by weighting each component based on impairment correlations.
That is, G-RAKE receivers suppress interference by optimally combining components of a received signal based on impairment correlations. The combining weights may also be used to estimate received signal quality, e.g., by calculating a Signal-to-Interference-plus-Noise Ratio (SINR). SINR information is provided to the corresponding base station for use in optimizing radio resources, e.g., by adjusting the power allocated to different channelization codes.
G-RAKE combining weights and SINR estimates are conventionally derived from channel response and impairment correlation estimates. Channel response and impairment correlation estimates are ascertained at least in part from demodulated and despread pilot symbols transmitted over a common channel. During the impairment correlation estimation process, interference and noise powers are conventionally estimated by fitting interference and noise correlation terms to a pilot symbol-based measured impairment. The model fitting parameter associated with the interference correlation term approximates total base station, not individual channelization code powers. As such, conventional combining weights and SINR estimates are based on total base station power approximations, not channel-specific power estimates.